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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy
Yanfei Li, Huipeng Shi, Feng-ze Han, et al.
Renewable Energy (2018) Vol. 135, pp. 540-553
Closed Access | Times Cited: 106

Showing 1-25 of 106 citing articles:

A review of deep learning for renewable energy forecasting
Huaizhi Wang, Zhenxing Lei, Xian Zhang, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111799-111799
Closed Access | Times Cited: 875
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Sheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 144, pp. 110992-110992
Closed Access | Times Cited: 407
Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM
Xiaolei Liu, Zi Lin, Zi‐Ming Feng
Energy (2021) Vol. 227, pp. 120492-120492
Open Access | Times Cited: 313
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, Viviana Cocco Mariani, et al.
Energy Conversion and Management (2020) Vol. 213, pp. 112869-112869
Closed Access | Times Cited: 187
Machine Learning and Deep Learning in Energy Systems: A Review
Mohammad Mahdi Forootan, Iman Larki, Rahim Zahedi, et al.
Sustainability (2022) Vol. 14, Iss. 8, pp. 4832-4832
Open Access | Times Cited: 158
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, et al.
Energy (2020) Vol. 216, pp. 119174-119174
Closed Access | Times Cited: 155
A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting
Hui Liu, Chengqing Yu, Haiping Wu, et al.
Energy (2020) Vol. 202, pp. 117794-117794
Closed Access | Times Cited: 147
A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators
Hoang-Phuong Nguyen, Jie Liu, Enrico Zio
Applied Soft Computing (2020) Vol. 89, pp. 106116-106116
Open Access | Times Cited: 145
Load Forecasting Techniques for Power System: Research Challenges and Survey
Naqash Ahmad, Yazeed Yasin Ghadi, Muhammad Adnan, et al.
IEEE Access (2022) Vol. 10, pp. 71054-71090
Open Access | Times Cited: 138
Knowledge structure and research progress in wind power generation (WPG) from 2005 to 2020 using CiteSpace based scientometric analysis
Ali Azam, Ammar Ahmed, Hao Wang, et al.
Journal of Cleaner Production (2021) Vol. 295, pp. 126496-126496
Closed Access | Times Cited: 136
The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer
Guihua Ban, Yan Chen, Zhenhua Xiong, et al.
Energy (2024) Vol. 290, pp. 130225-130225
Closed Access | Times Cited: 19
A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting
Shuai Zhang, Yong Chen, Wenyu Zhang, et al.
Information Sciences (2020) Vol. 544, pp. 427-445
Closed Access | Times Cited: 111
Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks
Qian Li, Jorge Loy-Benitez, KiJeon Nam, et al.
Energy (2019) Vol. 178, pp. 277-292
Open Access | Times Cited: 99
Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants
Hoang-Phuong Nguyen, Piero Baraldi, Enrico Zio
Applied Energy (2020) Vol. 283, pp. 116346-116346
Open Access | Times Cited: 88
An adaptive hybrid model for short term wind speed forecasting
Jinliang Zhang, Yi‐Ming Wei, Zhongfu Tan
Energy (2019) Vol. 190, pp. 115615-115615
Closed Access | Times Cited: 86
A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting
Ying Deng, Bo-Fu Wang, Zhiming Lü
Energy Conversion and Management (2020) Vol. 212, pp. 112779-112779
Closed Access | Times Cited: 82
Short-term average wind speed and turbulent standard deviation forecasts based on one-dimensional convolutional neural network and the integrate method for probabilistic framework
Xinyu Zhao, Na Jiang, Jinfu Liu, et al.
Energy Conversion and Management (2019) Vol. 203, pp. 112239-112239
Closed Access | Times Cited: 81
A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections
Hui Liu, Rui Yang, Tiantian Wang, et al.
Renewable Energy (2020) Vol. 165, pp. 573-594
Closed Access | Times Cited: 81
Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting
Peng Tian, Chu Zhang, Jianzhong Zhou, et al.
Renewable Energy (2020) Vol. 156, pp. 804-819
Closed Access | Times Cited: 74
Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
Sinvaldo Rodrigues Moreno, Viviana Cocco Mariani, Leandro dos Santos Coelho
Renewable Energy (2020) Vol. 164, pp. 1508-1526
Closed Access | Times Cited: 74
Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection
Chao Chen, Hui Liu
Energy Conversion and Management (2020) Vol. 206, pp. 112492-112492
Closed Access | Times Cited: 73
Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities
Nan Yang, Yang Cong, Chao Xing, et al.
IET Generation Transmission & Distribution (2021) Vol. 16, Iss. 4, pp. 629-640
Closed Access | Times Cited: 72
A novel loss function of deep learning in wind speed forecasting
Xi Chen, Ruyi Yu, Sajid Ullah, et al.
Energy (2021) Vol. 238, pp. 121808-121808
Closed Access | Times Cited: 69
Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models
Huaiping Jin, Lixian Shi, Xiangguang Chen, et al.
Renewable Energy (2021) Vol. 174, pp. 1-18
Closed Access | Times Cited: 61
Forecasting energy prices using a novel hybrid model with variational mode decomposition
Yu Lin, Qin Lu, Bin Tan, et al.
Energy (2022) Vol. 246, pp. 123366-123366
Closed Access | Times Cited: 39
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